The expected return of an investment portfolio is computed as the weighted average of the assets’ probable profits, weighted by the possible returns of every asset class. The formula used in calculating its value is E(R) = w1R1 + w2Rq + …+ wnRn.
For instance, there is a portfolio of two mutual funds, one for bonds investment and another for stocks. If you anticipated the fund for bonds to provide six percent profit and for stocks at 10 percent, with your 50 percent allotment for every asset class, you would come up with a filled-in formula of:
expected return (portfolio) = (0.1)*(0.5) + (0.06)*(0.5) = 0.08, or 8%.
However, you could not ensure that expected return would provide an accurate picture of return rate. It could still be utilized to project the portfolio’s anticipated value and guidelines from which to measure the actual profit.
Variance (σ2), on the other hand, is represented by the set of data points dispersion around their mean value. It is a projection of the squared deviations average from the mean. You could determine its value by calculating the squared deviations’ average weighted by probability from the expected figure. Specifically, the variance is used to specify the volatility or variability from an average. Volatility could be regarded as a risk measure, which means it could assist in measuring the risk you might take whenever you are about to purchase a stock.
Portfolio variance is a more advanced calculation since it involves the variance of component assets alongside their covariance measures. Covariance is defined as the metric for returns on two risky assets moving in the same direction. A positive measure of it indicates asset returns traverse together. Otherwise, there is an inverse movement from the asset returns.
Here’s formula to compute portfolio variance covering two assets:
(weight(1)^2*variance(1) + weight(2)^2*variance(2) + 2*weight(1)*weight(2)*covariance(1,2)
Standard deviation could be defined as a data set’s dispersion from its mean. It is the calculation of variance’s square root. The more distance there is from one data to another, the higher its deviation is. On the other hand, in financial terms, the standard deviation is employed to the annual rate of an investment return, which determines its volatility. It could also be termed as historical volatility and is utilized by investors to determine the anticipated volatility value.
The statistical measure provides light on historical volatility. For instance, a high standard deviation would be provided by a volatile stock, whereas a steadfast blue chip stock would have a lower figure. A large spread indicates how much the return of funds is diverting from the estimated normal returns.
With the given financial measurements, you could determine if a portfolio is containing a profitable mixture of investments or not.
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